Phytohormone Involvement in the Ustilago maydis– Zea mays Pathosystem: Relationships between Abscisic Acid and Cytokinin Levels and Strain Virulence in Infected Cob Tissue
Bibliographic record
Abstract
Ustilago maydis is the causative agent of common smut of corn. Early studies noted its ability to synthesize phytohormones and, more recently these growth promoting substances were confirmed as cytokinins (CKs). Cytokinins comprise a group of phytohormones commonly associated with actively dividing tissues. Lab analyses identified variation in virulence between U. maydis dikaryon and solopathogen infections of corn cob tissue. Samples from infected cob tissue were taken at sequential time points post infection and biochemical profiling was performed using high performance liquid chromatography-electrospray ionization tandem mass spectrometry (HPLC-ESI MS/MS). This hormone profiling revealed that there were altered levels of ABA and major CKs, with a marked reduction in CK glucosides, increases in methylthiol CKs and a particularly dramatic increase in cisZ CK forms, in U. maydis infected tissue. These changes were more pronounced in the more virulent dikaryon relative to the solopathogenic strain suggesting a role for cytokinins in moderating virulence during biotrophic infection. These findings highlight the fact that U. maydis does not simply mimic a fertilized seed but instead reprograms the host tissue. Results underscore the suitability of the Ustilago maydis- Zea mays model as a basis for investigating the control of phytohormone dynamics during biotrophic infection of plants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".